class_docstr: Tests the correctness of the Y = g(X) = Log[1 + exp(X)] transformation.
code: def testBijectorLogDetJacobianEventDimsZero(self):
      bijector = softplus.Softplus()
      y = 2 * rng.rand(2, 10)
      # No reduction needed if event_dims = 0.
      ildj = self._softplus_ildj_before_reduction(y)
  
      self.assertAllClose(
          ildj,
          self.evaluate(
              bijector.inverse_log_det_jacobian(y, event_ndims=0)
          ),
      )
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector_test_util
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 7 for calling the SoftplusBijectorTest obj's underlying member method code with example usage: obj.testBijectorLogDetJacobianEventDimsZero() without return types